AtliQ Hardware is a company with branches across India that offers computer hardware and peripheral devices to clients. However, their HR department is facing challenges in tracking employee attendance and identifying absenteeism patterns.

To address the problem statement, a data analyst has been assigned to the project to develop an attendance tracking system using Power BI & Excel. The system will provide a comprehensive overview of attendance records, including the number of leaves taken and reasons for absences. This will enable the HR department to identify patterns and improve attendance management.

A datasheet in Excel format has been provided for a three-month period between April and June 2022. The dataset includes information on employee codes, employee names, and their attendance records, which are represented by attendance keys such as “SL” for Sick Leave and “WFH” for Work From Home.

As a data analyst, it is important to thoroughly clean the dataset to ensure the accuracy and reliability of our findings. Therefore, we will be carefully examining the data and identifying any issues such as missing values, incorrect formatting, outliers, or duplicate entries. Once these problems have been addressed and the data is properly cleaned, we can move forward with analyzing and deriving insights from the dataset.

I have taken the following steps to clean and prepare the dataset for analysis:

Firstly, I have designated the first row as headers to improve the readability and organization of the data. Next, I have removed the first row as it is no longer necessary. I have also decided to remove the attendance key sheet as it is not useful for the upcoming data visualization phase. To better analyze the attendance records for each employee, I have unpivoted all rows except for the employee code and name for the months of June, May, and April 2022. Finally, I have created a function to ensure that any future data additions will not impact the analysis. After cleaning and preparing the dataset, the resulting table looks more organized and is ready for analysis.

1. calculate the percentage of employee attendance, sick leave, and work-from-home days taken.
2. We will then explore these percentages over time, examining the trends and patterns that emerge.
3. we will look for any seasonal or cyclical patterns in attendance, sick leave, and work-from-home days.

Based on the data analysis, the following insights were obtained:

1. There is a minor decline in the attendance of employees as time progresses
2. The trend of employees choosing to work from home has increased
3. There is a slight upward trend in sick leave taken by employees over time
4. Mondays & Tuesdays are the days when most employees were present in the office
5. Fridays were the days when most employees chose to work from home Mondays were the days when most employees took sick leaves.

it can be concluded that remote work is becoming more prevalent and acceptable in the workplace, and this trend is likely to continue. Additionally, it may be necessary for the organization to address the slight decrease in attendance and increase in sick leaves to ensure employee productivity and well-being. Finally, the insights regarding the days of the week when employees are present in the office or work from home, as well as the days when sick leaves are taken the most, can be useful in scheduling and resource allocation.

Medium : CLICK HERE.

Final Report File: CLICK HERE